I think it all boils down to, which is higher risk, using AI too much, or using AI too little? Right now I see the former as being hugely risky. Hallucinated bugs, coaxed into dead-end architectures, security concerns, not being familiar with the code when a bug shows up in production, less sense of ownership, less hands-on learning, etc. This is true both at the personal level and at the business level. (And astound…
> I think it all boils down to, which is higher risk, using AI too much, or using AI too little? This framing is exactly how lots of people in the industry are thinking about AI right now, but I think it's wrong. The way to adopt new science, new technology, new anything really, has always been that you validate it for small use cases, then expand usage from there. Test on mice, test in clinical trials, then go to ma…
Testing medical drugs is doing science. They test on mice because it's dangerous to test on humans, not to restrict scope to small increments. In doing science, you don't always want to be extremely cautious and incremental.
Trying to build a browser with 100 parallel agents is, in my view, doing science, more than adopting science. If they figure out that it can be done, then people will adopt it.
Trying to become a more productive engineer is adopting science, and your advice seems pretty solid here.